Download Plant organelle proteomics

Plant organelle proteomics
Kathryn S Lilley1 and Paul Dupree2
It is important for cell biologists to know the subcellular
localization of proteins to understand fully the functions of
organelles and the compartmentation of plant metabolism.
The accurate description of an organelle proteome requires the
ability to identify genuine protein residents. Such accurate
assignment is difficult in situations where a pure homogeneous
preparation of the organelle cannot be achieved. Practical
limitations in both organelle isolation and also analysis of low
abundance proteins have resulted in limited datasets from high
throughput proteomics approaches. Here, we discuss some
examples of quantitative proteomic methods and their use to
study plant organelle proteomes, with particular reference to
methods designed to give unequivocal assignments to
organelles.
Addresses
1
Cambridge Centre for Proteomics, Cambridge Systems Biology
Centre, University of Cambridge, Cambridge CB2 1QR, United Kingdom
2
Department of Biochemistry, University of Cambridge, Building O,
Downing Site, Cambridge CB2 1QW, United Kingdom
Corresponding author: Lilley, Kathryn S ([email protected]) and
Dupree, Paul ([email protected])
Current Opinion in Plant Biology 2007, 10:594–599
This review comes from a themed issue on
Cell Biology
Edited by Ben Scheres and Volker Lipka
Available online 2nd October 2007
1369-5266/$ – see front matter
Crown Copyright # 2007 Published by Elsevier Ltd. All rights reserved.
DOI 10.1016/j.pbi.2007.08.006
Introduction
There is a plethora of information about cellular mechanisms in plants that we glean by studying mutants or
changes in transcript levels, especially in Arabidopsis
thaliana. The study of genes however is only a limited
dimension for cell biologists. The study of the proteome
is far more information rich as one gene does not necessarily give rise to a single protein isoform [1]. Additionally,
proteomic studies allow the determination of post translation modifications such as phosphorylation [2], ubiquitination [3], acylation and proteolytic processing, as well
as information about which proteins exist together in
complexes. Here, we discuss another important dimension that is measurable only by studying the proteome,
namely spatial resolution; where proteins reside within
cells and the trafficking of proteins between compartments during normal cellular processes and after specific
perturbation.
Current Opinion in Plant Biology 2007, 10:594–599
Assigning a subcellular location to a protein is very
desirable to biologists for two reasons. Firstly, it can help
elucidate their role in the cell as proteins are spatially
organised according to their function [4]. Secondly, it
refines our knowledge of cellular processes by pinpointing certain activities to specific organelles [5]. Traditional
methods to assign proteins to subcellular locations are
mostly targeted to a single protein of interest, for example
creating a GFP-tagged version or raising a specific antibody. The SUBA database, which allows easy access to
collated information from the literature on subcellular
localisation of Arabidopsis proteins, lists more than 1300
proteins studied with GFP fusions [6]. The data in
SUBA indicates that the GFP approach has been particularly successful in studying nuclear proteins, with nearly
400 proteins localised. However, over a quarter of the
GFP fusion proteins had cytosolic or unclear localisations,
and many more have conflicting localisations reported
[6]. Although there are several GFP-fusion screening
programmes underway, they also have yet to report many
localisations of organelle membrane proteins ([7,8] Protlocdb: http://bioinf.scri.ac.uk/cgi-bin/ProtLocDB/home).
The relatively slow progress may reflect difficulties when
expressing GFP-fusions to membrane proteins, where
transient over expression, often in a foreign species and
cell type, can lead to misleading results.
In its simplest form, organelle proteomics involves isolating the organelle of interest and producing a catalogue of
the proteins present in that organelle by some form of
separation of proteins or their proteolytic fragments followed by identification utilizing mass spectrometry. In
order for an organelle protein catalogue to be useful to
biologists, it is essential to determine the specific localization of a protein with high confidence. If this is to be
achieved by using a proteomics approach, the organelle
preparation must be free from contamination from other
organelle types. In plants however, as in every other
eukaryotic system, some organelles such as the nucleus
[9], mitochondria [10], and chloroplasts [11] are relatively
easy to obtain in a pure form, whereas many endomembrane organelles are impossible to purify without considerable contamination from other organelles with similar
densities [12–14]. For example, Golgi-fractions enriched
over 100-fold from a cellular homogenate of Arabidopsis,
still contain a minority of probable Golgi proteins (Authors’
unpublished data) (Figure 1a). Furthermore, proteins in
the secretory and endocytic pathway may traffic through
several organelles of the endomembrane system en route to
their final destination. A final confounding factor is
that some proteins within the endomembrane system
cycle between compartments, for example endoplasmic
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Plant organelle proteomics Lilley and Dupree 595
Figure 1
Locations of proteins in multiple organelles of Arabidopsis thaliana. (a) Proteins identified in an organelle-enriched fraction may be derived from
multiple compartments. Proteins in an approximately 100-fold Golgi-enriched sample from Arabidopsis callus homogenate were catalogued after
identification by mass spectrometry. (i) A depiction of the number of proteins from this fraction with known or predicted localizations based on
interrogation of the literature and proteins of no known location (80% of the total). (ii) The percentage of proteins with known or predicted
locations within the ER, PM, vacuole, Golgi and mitochondria and plastids. (b) PCA plot of the LOPIT data of Dunkley et al. [28]. Proteins that
co-fractionate in density gradients appear clustered together. The proteins predicted by multivariate statistical approaches to be localized in
various organelles are highlighted.
reticulum (ER) residents continuously escape to the Golgi
and are retrieved in COPI vesicles [15], and some plasma
membrane proteins are endocytosed and then return to
the plasma membrane (PM). Furthermore, some proteins
are dual targeted to mitochondria and plastids [16].
Approaches at organelle proteomics that attempt purification and analysis of a single organelle will therefore
not reveal the broader picture, and could be misleading.
These factors necessitate the measurement of the steady
state distributions of proteins within the whole endomembrane system in order to obtain a realistic insight into the
principal subcellular localization of individual endomembrane proteins. Novel proteins identified in many organelle
proteomic studies cannot be confidently assigned to the
organelle where the extent of contamination is not assessed
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by the use of an appropriate control, or unless the assigned
localization is subsequently confirmed by microscopy.
Methods to study the organelle proteome
The repertoire of techniques utilized in proteomics studies
continues to expand at a considerable rate. These techniques can be subdivided into those which are used ostensibly for cataloguing proteins within a given sample and
those which allow comparative or quantitative analyses of
proteomes. The ability of a technique to be applied in a
quantitative manner is of particular importance in many
proteomics studies as such technologies allow incorporation of control samples within an experimental design. In
the context of organelle proteomics, the use of such a
control allows comparison of the specificity of proteins
Current Opinion in Plant Biology 2007, 10:594–599
596 Cell Biology
in the sample, and is necessary for judging whether the
presence indicates a specific enrichment in the sample or
that the protein is a contaminating abundant protein.
Technologies can be divided still further into those which
are polyacrylamide gel-based and those which are non-gel
based.
Traditionally, two-dimensional polyacrylamide gel electrophoresis (2D PAGE) has been the protein separation
technique most associated with proteomic studies [17]
and remains one of the key methodologies in proteomics
studies. A major drawback of using 2D gels is their
incompatibility with hydrophobic membrane proteins
which make up a mechanistically important subset of
proteins and are likely to play crucial roles within organelles [18].
Non-2D gel based technologies therefore are more attractive methodologies to employ in a global study of the
subcellular proteomes as they do not suffer the same bias
towards analysis of the soluble proteome as 2D gels
provided that the proteins are successfully solubilized
before analysis [19].
Typically in non-2D gel based approaches, the proteome
undergoes simplification before mass spectrometric
analysis in order to maximize the amount of information
about the protein content of the sample that can be
achieved. These methods are often referred to as shotgun proteomics. One such approach, MudPIT (multidimensional protein identification technology [20]),
involves a solution phase digestion of proteins to peptides
and then multi-dimensional chromatographic separation
of peptides before mass spectrometric analysis.
These methods can be used in a quantitative manner by
coupling them with either the use of differential stable
isotopes, or label free technologies. In both cases the
abundance of peptides is calculated from mass spectrometric measurements.
Stable isotope labelling involves quantitation using differential incorporation of stable isotopes either in vivo or in
vitro. There are several ways in which this can be achieved.
One method involves the growth of cultures in the presence of a defined medium containing a heavy isotope,
typically 15N ([21,22]. Samples grown in the presence of
the natural isotope and the heavy isotope can be pooled,
reduced to peptides, and the peptides separated by multidimensional liquid chromatography before application to
mass spectrometry. The relative abundance of a peptide
generated from a protein within cultures being compared is
then calculated by measuring ion intensities of the ‘light’
and ‘heavy’ versions of the same peptide.
A more widely applicable variation of this method is to
label extracted protein with tags which can be produced
Current Opinion in Plant Biology 2007, 10:594–599
in more than one isotopic form. One such method
involves the incorporation of 16O or 18O during trypsinolysis. The most commonly used tagging system, however,
involves the use of amine modifying labelling reagents for
multiplexed relative and absolute protein quantitation
(iTRAQ) reagents, which are a multiplexed set of four
isotope tags which labelled peptides generated from
extracted proteins by trypsin [23]. Since the iTRAQ tags
are isobaric, differentially labelled versions of a peptide
appear as a single precursor ion peak. When an iTRAQlabelled peptide is subjected to collision-induced dissociation in MS/MS mode, the iTRAQ tags release diagnostic, low-mass ions (reporter ions) that are used for
quantitation.
Label free quantitation is based entirely on peak intensity
measurements of peptides detected by mass spectrometry [24,25] or on the number of ions per protein (spectral
counts) detected in a mass spectrometric experiment [26].
Each of the quantitative methods listed above have
differing and, in many cases, complementary strengths
and weaknesses. A full description of these is outside the
scope of this manuscript, but is explored in many publications ([18] and others).
High throughput organelle proteomics
methodologies
Recently several high throughput methods have emerged
involving quantitative strategies, which have overcome
the need to produce a pure organelle for analysis. Each
of these methods relies on quantitative proteomics to
characterize the distribution pattern of organelles amongst
partially enriched fractions generated by various separation
technologies and have the potential to discriminate between genuine organelle residents and contaminants without preparation of pure organelles [27,28,29,30]. The
methods developed by Gilchrist et al. and Foster et al. used
samples generated from rat liver and both use label-free
quantitation.
The method of Dunkley et al., named localization of
organelle proteins by isotope tagging (LOPIT), used callus
tissue derived from Arabidopsis roots [27,28]. LOPIT has
resulted in the first large scale data set enabling the
simultaneous assignment of proteins to multiple subcellular locations with a high degree of confidence. The
general principle of LOPIT relies on analysis of the
distribution of organelle proteins within fractions from
self-generating iodixanol density gradients. Organelle
distributions are first visualized by Western blotting with
antibodies specific to known marker proteins. Four fractions, enriched with different organelles, are initially
selected for comparison. Additional overlapping comparisons can then be carried out to cover a wider area of the
gradient. Unlike the label-free approach taken by Gilchrist
and Foster and their co-workers, protein distributions are
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then determined by measuring their relative abundance
using iTRAQ reagents and tandem mass spectrometry
(MS/MS). Multivariate statistical techniques such as
principal components analysis (PCA) and partial least
squares discriminant analysis (PLS-DA) are employed
to cluster proteins according to the similarities in their
gradient distributions and thus to assign proteins to
organelles. Figure 1b shows a PCA plot generated using
the LOPIT approach to analyse protein localization
within Arabidopsis callus in the authors’ laboratories.
The LOPIT data set of Dunkley et al., provides localization information for 527 proteins in total, 182 ER, 92
PM proteins, 89 Golgi proteins, 24 tonoplast proteins,
and 140 mitochondria/plastid proteins which co-cluster
in this particular analysis (Figure 1b) [28]. The
majority of these assignments presented proteins for
which there was no previous localization data. The
predictions of LOPIT were subsequently validated by
microscopy. In 16 of the 18 cases that gave a clear result,
GFP fusions were targeted to the LOPIT-predicted
organelle. We have recently compared this LOPIT data
set with all the data on localization by GFP-tagging in
SUBA (as of May 2007). Of the 1349 GFP-tagged
proteins in the SUBA database, 50 were assigned to
specific organelles in both the GFP experiments and the
LOPIT dataset. The localizations were consistent in
60% of the cases. Some of the discrepancy may be
because of incorrect predictions from the proteomic
datasets. However, the possibility of incorrect localization of GFP fusions must also be considered. In most
cases where discrepancies have been investigated, the
transiently expressed GFP-fusion has not reflected the
steady state localization of the endogenous protein
(Authors’ unpublished observations).
The plant organelle proteome
Many researchers have taken an approach where a
single organelle is the focus of the study. In the cases
where highly efficient organelles preparation has been
possible robust catalogues have been achieved, but in all
cases the robustness of this dataset can only be assessed
by inclusion of appropriate control samples into the
experimental schema.
Pendle and co-workers, for example, carried out analysis
of the Arabidopsis nucleolus proteome [31]. A highly
efficient nucleolus purification process allowed over
200 proteins to be identified. However, there was no
control sample for the purification, and thus the authors
attempted confirmation of localization of 72 proteins with
microscopy of GFP fusions, of which 87% showed some
nucleolar labelling. Furthermore, since two thirds of the
proteins had a direct counterpart in the human nucleolar
proteome, this is a dataset with high confidence assignments, although given the weak nucleolar labelling of
some of the proteins, the steady state proportion of some
of the proteins in the nucleolus might be low.
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Mitochondria and plastids can be obtained relatively
purely, and there are several excellent databases of proteomic identified proteins from these organelles [32,33].
There are promising purification approaches to isolating
plastids from specialized cell types which may allow
studies of plastid subtype proteomes [34]. Plastid subproteomes have also been studied, including stromal,
envelope, thylakoid proteins and the plastoglobules: lipoprotein particles present in chloroplasts. Ytterberg et al.
used stable isotopic labelling of peptides with formaldehyde to investigate the change in protein composition
of highly enriched plastoglobule preparations between
different light stress regimes [35]. These preparations
were apparently free of contaminating membranes from
other organelles, although some of the proteins have been
found in proteomic studies of thylakoids, stroma and
plastid envelopes. It is unclear whether plastoglobules
are cross contaminants of those suborganelle preparations,
or whether proteins are shared in localization.
In studies that focus on organelles which are impossible to
isolate to such a degree of purity, more stringent controls
have to be taken to avoid mis-assignment of proteins to
subcellular locations. The tonoplast has been subject of
many plant subcellular localization studies, because of the
importance of the tonoplast in plant cell metabolism and
the enigmatic identity of many of the membrane transport proteins. Several vacuole proteomic studies have
been attempted and yielded large datasets. A particularly
successful recent example was the identification of
HvSUT2 and AtSUT4 as tonoplast sucrose transporters
after proteomic analysis of barley tonoplasts [36]. The
localization was supported by expression of GFP fusion
proteins. However, in general vacuolar purification is
difficult, and there is often some contamination within
the datasets from other organelles, possibly because the
vacuole has an autophagic function and therefore proteins
from elsewhere within the cell will co-purify in the
vacuolar lumen [37,38]. Of the proteins found in
the vacuolar fraction of the dataset of Jaquinod et al, there
is an overlap of 142 proteins with the LOPIT set. Only 22
proteins however are in agreement with Dunkley et al.’s
vacuolar list, the remainder includes 90 which were
classified by LOPIT not vacuolar in location. Attempts
to further purify the organelle, for example using free flow
zonal electrophoresis [39], may assist, but will not obviate
the need for controls for the specific enrichment of the
majority of the protein in the vacuoles.
Several papers have characterized the PM proteome using
a range of quantitative techniques. The group of Sussman
used trypsin-catalysed 18O labelling to address the issue
of quantitative enrichment of proteins in dextran-PEG
preparation of PM from Arabidopsis [40]. On the basis of
low quantitative enrichment, the authors were able to
disregard the majority of the proteins in the sample, and
Current Opinion in Plant Biology 2007, 10:594–599
598 Cell Biology
identified 70 proteins as enriched more than the bona fide
PM protein, the proton pumping ATPase. There were 29
of these 70 proteins in common with the LOPIT dataset,
of which 23 were predicted PM by LOPIT, indicating a
very high level of consistency between these quantitative
methods. Lanquar and co-workers showed that a change
in composition of PM protein samples on cadmium stress
could be detected using 15N stable isotopic labelling of
proteins in culture, followed by PM preparation and MS
[41]. More recently, Benschop et al (2007) used similar
stable isotopic labelling of Arabidopsis tissue culture
cells, and identified over 1000 proteins in their PM
samples [21]. However, their aim was to use the quantitative analysis coupled with phosphoproteomics to
identify proteins in PM preparations that were differentially phosphorylated in response to flagellin peptide or
xylanase treatment. Of the proteins in common with the
LOPIT dataset, nearly 40% were predicted PM by Dunkley and co-workers, suggesting a good enrichment of PM
in the analysis.
The lipid raft hypothesis proposes that certain proteins
are trafficked to the PM in membrane domains of specialized lipid and protein composition. This clustering may
also be important during endocytosis. These specialized
membrane domains have been studied by preparation of
detergent resistant membrane (DRM) domains. In Arabidopsis, these have been prepared from a mixture of
organelles, and shown to be largely derived from the PM
[42]. DRMs have also been isolated from PM preparations
from tobacco [43] and Medicago truncatula [44], and the
authors suggest they contain proteins involved in signalling processes. To understand precisely which plasma
membrane proteins use these membrane domains, it will
be interesting to see the specificity of enrichment of the
proteins in the DRMs by application of quantitative
proteomics.
Conclusions
Undoubtedly, by allowing protein quantity in samples to
be compared, quantitative approaches are improving the
quality of organelle proteome datasets. The alternatives
of stable isotopic labelling in culture or after sample
preparation, as well as label-free techniques, will all
contribute flexibility of the experimental design. Ideally,
quantitative techniques for organelle dynamics need to
be able to compare both enrichment of organelles with a
control and also change between control and treatment in
the same analysis. One of the advantages of LOPIT over
alternative quantitative techniques is that is provides
information on the steady state localization of proteins
in cells. Proteins found in multiple locations in the cell
will appear at an intermediate position in the analysis
plots. Therefore, LOPIT appears to be suited to addressing questions where the localization of proteins changes
in different conditions, for example, in stress, drug treatment, or in comparison of mutants.
Current Opinion in Plant Biology 2007, 10:594–599
References and recommended reading
Papers of particular interest, published within the annual period of
review, have been highlighted as:
of special interest
of outstanding interest
1.
Peck SC: Update on proteomics in arabidopsis. Where do we
go from here? Plant Physiol 2005, 138:591-599.
2.
Nuhse TS, Stensballe A, Jensen ON, Peck SC:
Phosphoproteomics of the arabidopsis plasma membrane
and a new phosphorylation site database. Plant Cell 2004,
16:2394-2405.
3.
Kwon SJ, Choi EY, Choi YJ, Ahn JH, Park OK: Proteomics studies
of post-translational modifications in plants. J Exp Bot 2006,
57:1547-1551.
4.
Dreger M: Subcellular proteomics. Mass Spectrom Rev 2003,
22:27-56.
5.
Lunn JE: Compartmentation in plant metabolism. J Exp Bot
2007, 58:35-47.
6.
Heazlewood JL, Verboom RE, Tonti-Filippini J, Small I, Millar AH:
SUBA: The Arabidopsis subcellular database. Nucleic Acids
Res 2007, 35:D213-D218.
The SUBA database allows easy access to collated information from the
literature on subcellular localisation of Arabidopsis proteins.
7.
Li SJ, Ehrhardt DW, Rhee SY: Systematic analysis of
Arabidopsis organelles and a protein localization database for
facilitating fluorescent tagging of full-length Arabidopsis
proteins. Plant Physiol 2006, 141:527-539.
8.
Koroleva OA, Tomlinson ML, Leader D, Shaw P, Doonan JH: Highthroughput protein localization in Arabidopsis using
Agrobacterium-mediated transient expression of GFP-ORF
fusions. Plant J 2005, 41:162-174.
9.
Bae MS, Cho EJ, Choi EY, Park OK: Analysis of the Arabidopsis
nuclear proteome and its response to cold stress. Plant J 2003,
36:652-663.
10. Millar AH, Heazlewood JL, Kristensen BK, Braun HP, Moller IM:
The plant mitochondrial proteome. Trends Plant Sci 2005,
10:36-43.
11. van Wijk KJ: Plastid proteomics. Plant Physiol Biochem 2004,
42:963-977.
12. Wu CC, MacCoss MJ, Mardones G, Finnigan C, Mogelsvang S,
Yates JR, Howell KE: Organellar proteomics reveals Golgi
arginine dimethylation. Mole Biol Cell 2004, 15:2907-2919.
13. Pan SQ, Carter CJ, Raikhel NV: Understanding protein
trafficking in plant cells through proteomics. Exp Rev Proteom
2005, 2:781-792.
14. Szponarski W, Sommerer N, Boyer JC, Rossignol M, Gibrat R:
Large-scale characterization of integral proteins from
Arabidopsis vacuolar membrane by two-dimensional liquid
chromatography. Proteomics 2004, 4:397-406.
15. Hanton SL, Bortolotti LE, Renna L, Stefano G, Brandizzi F:
Crossing the divide – Transport between the endoplasmic
reticulum and Golgi apparatus in plants. Traffic 2005,
6:267-277.
16. Millar AH, Whelan J, Small I: Recent surprises in protein
targeting to mitochondria and plastids. Curr Opin Plant Biol
2006, 9:610-615.
17. Klose J, Nock C, Herrmann M, Stuhler K, Marcus K, Bluggel M,
Krause E, Schalkwyk LC, Rastan S, Brown SDM et al.: Genetic
analysis of the mouse brain proteome. Nat Genet 2002,
30:385-393.
18. Lilley KS, Dupree P: Methods of quantitative proteomics and
their application to plant organelle characterization. J Exp Bot
2006, 57:1493-1499.
19. Mitra SK, Gantt JA, Ruby JF, Clouse SD, Goshe MB: Membrane
proteomic analysis of Arabidopsis thaliana using alternative
solubilization techniques. J Proteome Res 2007, 6:1933-1950.
www.sciencedirect.com
Edited by Foxit Reader
Copyright(C) by Foxit Software Company,2005-2007
organelle proteomics Lilley and Dupree 599
For EvaluationPlant
Only.
20. Washburn MP, Wolters D, Yates JR: Large-scale analysis of the
yeast proteome by multidimensional protein identification
technology. Nat Biotechnol 2001, 19:242-247.
21. Benschop JJ, Mohammed S, O’Flaherty M, Heck AJR, Slijper M,
Menke FLH: Quantitative phospho-proteomics of early
elicitor signalling in Arabidopsis. Mole Cellular Proteom 2007,
7:1198-1214.
An excellent study where 15N stable isotope labelling of cultured Arabidopsis cells was used to identify phosphorylated proteins in fractions
enriched for plasma membrane proteins that were differentially phosphorylated in response to a flagellin peptide or xylanase treatment.
22. Nelson CJ, Huttlin EL, Hegeman AD, Harms AC, Sussman MR:
Implications of N-15-metabolic labeling for automated
peptide identification in Arabidopsis thaliana. Proteomics
2007, 7:1279-1292.
23. Ross PL, Huang YLN, Marchese JN, Williamson B, Parker K,
Hattan S, Khainovski N, Pillai S, Dey S, Daniels S et al.:
Multiplexed protein quantitation in Saccharomyces cerevisiae
using amine-reactive isobaric tagging reagents. Mole Cellular
Proteom 2004, 3:1154-1169.
24. Old WM, Meyer-Arendt K, Aveline-Wolf L, Pierce KG, Mendoza A,
Sevinsky JR, Resing KA, Ahn NG: Comparison of label-free
methods for quantifying human proteins by shotgun
proteomics. Mole Cellular Proteom 2005, 4:1487-1502.
25. Le Bihan T, Goh T, Stewart II, Salter AM, Bukhman YV, Dharsee M,
Ewing R, Wisniewski JR: Differential analysis of membrane
proteins in mouse fore- and hindbrain using a label-free
approach. J Proteome Res 2006, 5:2701-2710.
26. Kislinger T, Cox B, Kannan A, Chung C, Hu PZ, Ignatchenko A,
Scott MS, Gramolini AO, Morris Q, Hallett MT et al.: Global survey
of organ and organelle protein expression in mouse:
Combined proteomic and transcriptomic profiling. Cell 2006,
125:173-186.
27. Dunkley TPJ, Watson R, Griffin JL, Dupree P, Lilley KS:
Localization of organelle proteins by isotope tagging (LOPIT).
Mole Cellular Proteom 2004, 3:1128-1134.
28. Dunkley TPJ, Hester S, Shadforth IP, Runions J, Weimar T,
Hanton SL, Griffin JL, Bessant C, Brandizzi F, Hawes C et al.:
Mapping the Arabidopsis organelle proteome. Proc Natl Acad
Sci U S A 2006, 103:6518-6523.
The first comprehensive catalogue of genuine residents of organelles
within Arabidopsis cells. The authors used their localization of organelle
proteins by isotope tagging (LOPIT) technology to determine the steady
locations of 537 proteins, the majority of which being membrane or
membrane associated proteins.
29. Foster LJ, de Hoog CL, Zhang YL, Zhang Y, Xie XH, Mootha VK,
Mann M: A mammalian organelle map by protein correlation
profiling. Cell 2006, 125:187-199.
30. Gilchrist A, Au CE, Hiding J, Bell AW, Fernandez-Rodriguez J,
Lesimple S, Nagaya H, Roy L, Gosline SJC, Hallett M et al.:
Quantitative proteomics analysis of the secretory pathway.
Cell 2006, 127:1265-1281.
31. Pendle AF, Clark GP, Boon R, Lewandowska D, Lam YW,
Andersen J, Mann M, Lamond AI, Brown JWS, Shaw PJ:
Proteomic analysis of the Arabidopsis nucleolus suggests
novel nucleolar functions. Mole Biol Cell 2005, 16:260-269.
32. Heazlewood JL, Tonti-Filippini JS, Gout AM, Day DA, Whelan J,
Millar AH: Experimental analysis of the Arabidopsis
www.sciencedirect.com
mitochondrial proteome highlights signaling and regulatory
components, provides assessment of targeting prediction
programs, and indicates plant-specific mitochondrial
proteins. Plant Cell 2004, 16:241-256.
33. Sun Q, Emanuelsson O, van Wijk KJ: Analysis of curated and
predicted plastid subproteomes of Arabidopsis. Subcellular
compartmentalization leads to distinctive proteome
properties. Plant Physiol 2004, 135:723-734.
34. Truernit E, Hibberd JM: Immunogenic tagging of chloroplasts
allows their isolation from defined cell types. Plant J 2007,
50:926-932.
35. Ytterberg AJ, Peltier JB, van Wijk KJ: Protein profiling of
plastoglobules in chloroplasts and chromoplasts. A surprising
site for differential accumulation of metabolic enzymes. Plant
Physiol 2006, 140:984-997.
36. Endler A, Meyer S, Schelbert S, Schneider T, Weschke W,
Peters SW, Keller F, Baginsky S, Martinoia E, Schmidt UG:
Identification of a vacuolar sucrose transporter in barley and
arabidopsis mesophyll cells by a tonoplast proteomic
approach. Plant Physiol 2006, 141:196-207.
37. Carter C, Pan SQ, Jan ZH, Avila EL, Girke T, Raikhel NV: The
vegetative vacuole proteorne of Arabidopsis thaliana
reveals predicted and unexpected proteins. Plant Cell 2004,
16:3285-3303.
38. Jaquinod M, Villiers F, Kieffer-Jaquinod S, Hugouvieu V, Bruley C,
Garin J, Bourguignon J: A proteomics dissection of Arabidopsis
thaliana vacuoles isolated from cell culture. Mole Cell Proteom
2007, 6:394-412.
39. Barkla BJ, Vera-Estrella R, Pamtoja O: Enhanced separation of
membranes during free flow zonal electrophoresis in plants.
Anal Chem 2007, 79(14):5181-5187.
40. Nelson CJ, Hegeman AD, Harms AC, Sussman MR: A
quantitative analysis of Arabidopsis plasma membrane using
trypsin-catalyzed O-18 labeling. Mole Cell Proteom 2006,
5:1382-1395.
A study in which 18O labelling during trypsinolysis was employed to
address the issue of quantitative enrichment of proteins in plasma
membrane preparations. This lead to the assignment of genuine residents
of the plasma membrane of Arabidopsis seedlings.
41. Lanquar V, Kuhn L, Lelievre F, Khafif M, Espagne C, Bruley C,
Barbier-Brygoo H, Garin J, Thomine S: N-15-Metabolic labeling
for comparative plasma membrane proteomics in Arabidopsis
cells. Proteomics 2007, 7:750-754.
42. Borner GHH, Sherrier DJ, Weimar T, Michaelson LV,
Hawkins ND, MacAskill A, Napier JA, Beale MH, Lilley KS,
Dupree P: Analysis of detergent-resistant membranes in
Arabidopsis. Evidence for plasma membrane lipid rafts.
Plant Physiol 2005, 137:104-116.
43. Morel J, Claverol S, Mongrand S, Furt F, Fromentin J, Bessoule JJ,
Blein JP, Simon-Plas F: Proteomics of plant detergent-resistant
membranes. Mole Cellular Proteom 2006, 5:1396-1411.
44. Lefebvre B, Furt F, Hartmann MA, Michaelson LV, Carde JP,
Sargueil-Boiron F, Rossignol M, Napier JA, Cullimore J,
Bessoule JJ et al.: Characterization of lipid rafts from Medicago
truncatula root plasma membranes: A proteomic study
reveals the presence of a raft-associated redox system.
Plant Physiol 2007, 144:402-418.
Current Opinion in Plant Biology 2007, 10:594–599